Rodrigo Rodriguez (Pragmatismo) d0563391b6 ``` Add comprehensive email account management and user settings

interface

Implements multi-user authentication system with email account
management, profile settings, drive configuration, and security
controls. Includes database migrations for user accounts, email
credentials, preferences, and session management. Frontend provides
intuitive UI for adding IMAP/SMTP accounts with provider presets and
connection testing. Backend supports per-user vector databases for email
and file indexing with Zitadel SSO integration and automatic workspace
initialization. ```

2025-11-21 09:28:35 -03:00

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Raw Blame History

🧠 Knowledge Base (KB) System - Complete Implementation

Overview

The KB system allows .bas tools to dynamically add/remove Knowledge Bases to conversation context using ADD_KB and CLEAR_KB keywords. Each KB is a vectorized folder that gets queried by the LLM during conversation.

🏗️ Architecture

work/
  {bot_name}/
    {bot_name}.gbkb/          # Knowledge Base root
      circular/               # KB folder 1
        document1.pdf
        document2.md
        vectorized/           # Auto-generated vector index
      comunicado/             # KB folder 2
        announcement1.txt
        announcement2.pdf
        vectorized/
      geral/                  # KB folder 3
        general1.md
        vectorized/

📊 Database Tables (Already Exist!)

From Migration 6.0.2 - `kb_collections`

kb_collections
  - id (uuid)
  - bot_id (uuid)
  - name (text)                    -- e.g., "circular", "comunicado"
  - folder_path (text)             -- "work/bot/bot.gbkb/circular"
  - qdrant_collection (text)       -- "bot_circular"
  - document_count (integer)

From Migration 6.0.2 - `kb_documents`

kb_documents
  - id (uuid)
  - bot_id (uuid)
  - collection_name (text)         -- References kb_collections.name
  - file_path (text)
  - file_hash (text)
  - indexed_at (timestamptz)

NEW Migration 6.0.7 - `session_kb_associations`

session_kb_associations
  - id (uuid)
  - session_id (uuid)              -- Current conversation
  - bot_id (uuid)
  - kb_name (text)                 -- "circular", "comunicado", etc.
  - kb_folder_path (text)          -- Full path to KB
  - qdrant_collection (text)       -- Qdrant collection to query
  - added_at (timestamptz)
  - added_by_tool (text)           -- Which .bas tool added this KB
  - is_active (boolean)            -- true = active in session

🔧 BASIC Keywords

`ADD_KB kbname`

Purpose: Add a Knowledge Base to the current conversation session

Usage:

' Static KB name
ADD_KB "circular"

' Dynamic KB name from variable
kbname = LLM "Return one word: circular, comunicado, or geral based on: " + subject
ADD_KB kbname

' Multiple KBs in one tool
ADD_KB "circular"
ADD_KB "geral"

What it does:

Checks if KB exists in kb_collections table
If not found, creates entry with default path
Inserts/updates session_kb_associations with is_active = true
Logs which tool added the KB
KB is now available for LLM queries in this session

Example (from change-subject.bas):

PARAM subject as string
DESCRIPTION "Called when someone wants to change conversation subject."

kbname = LLM "Return one word circular, comunicado or geral based on: " + subject
ADD_KB kbname

TALK "You have chosen to change the subject to " + subject + "."

`CLEAR_KB [kbname]`

Purpose: Remove Knowledge Base(s) from current session

Usage:

' Remove specific KB
CLEAR_KB "circular"
CLEAR_KB kbname

' Remove ALL KBs
CLEAR_KB

What it does:

Sets is_active = false in session_kb_associations
KB no longer included in LLM prompt context
If no argument, clears ALL active KBs

Example:

' Switch from one KB to another
CLEAR_KB "circular"
ADD_KB "comunicado"

' Start fresh conversation with no context
CLEAR_KB
TALK "Context cleared. What would you like to discuss?"

🤖 Prompt Engine Integration

How Bot Uses Active KBs

When building the LLM prompt, the bot:

Gets Active KBs for Session:

let active_kbs = get_active_kbs_for_session(&conn_pool, session_id)?;
// Returns: Vec<(kb_name, kb_folder_path, qdrant_collection)>
// Example: [("circular", "work/bot/bot.gbkb/circular", "bot_circular")]

Queries Each KB's Vector DB:

for (kb_name, _path, qdrant_collection) in active_kbs {
    let results = qdrant_client.search_points(
        qdrant_collection,
        user_query_embedding,
        limit: 5
    ).await?;
    
    // Add results to context
    context_docs.extend(results);
}

Builds Enriched Prompt:

System: You are a helpful assistant.

Context from Knowledge Bases:
[KB: circular]
- Document 1: "Circular 2024/01 - New policy regarding..."
- Document 2: "Circular 2024/02 - Update on procedures..."

[KB: geral]
- Document 3: "General information about company..."

User: What's the latest policy update?

4.3 KiB Raw Blame History